Book Image

Advanced Deep Learning with Keras

By : Rowel Atienza
Book Image

Advanced Deep Learning with Keras

By: Rowel Atienza

Overview of this book

Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
Table of Contents (13 chapters)

Implementation of StackedGAN in Keras

The detailed network model of StackedGAN can be seen in the following figure. For conciseness, only two encoder-GANs per stack are shown. The figure may initially appear complex, but it is just a repetition of an encoder-GAN. Meaning that if we understood how to train one encoder-GAN, the rest uses the same concept. In the following section, we assume that the StackedGAN is designed for the MNIST digit generation:

Figure 6.2.2: A StackedGAN is made of a stack of an encoder and GAN. The encoder is pre-trained to perform classification. Generator1, G1, learns to synthesize f1f features conditioned on the fake label, y f, and latent code, z1f. Generator0, G0, produces fake images using both the fake features, f1f and latent code, z0f.

StackedGAN starts with an Encoder. It could be a trained classifier that predicts the correct labels. The intermediate features vector, f1r, is made available for GAN training. For MNIST, we can use a CNN-based classifier similar...